{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "# Reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "**Note: For the three distance computations that we require you to implement in this notebook, you may not use the np.linalg.norm() function that numpy provides.**\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1** \n",
    "\n",
    "Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*\n",
    "\n",
    "- the two images are pretty diff\n",
    "- distance between one training data and all test images\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 139 / 500 correct => accuracy: 0.278000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 2**\n",
    "\n",
    "We can also use other distance metrics such as L1 distance.\n",
    "For pixel values $p_{ij}^{(k)}$ at location $(i,j)$ of some image $I_k$, \n",
    "\n",
    "the mean $\\mu$ across all pixels over all images is $$\\mu=\\frac{1}{nhw}\\sum_{k=1}^n\\sum_{i=1}^{h}\\sum_{j=1}^{w}p_{ij}^{(k)}$$\n",
    "And the pixel-wise mean $\\mu_{ij}$ across all images is \n",
    "$$\\mu_{ij}=\\frac{1}{n}\\sum_{k=1}^np_{ij}^{(k)}.$$\n",
    "The general standard deviation $\\sigma$ and pixel-wise standard deviation $\\sigma_{ij}$ is defined similarly.\n",
    "\n",
    "Which of the following preprocessing steps will not change the performance of a Nearest Neighbor classifier that uses L1 distance? Select all that apply.\n",
    "1. Subtracting the mean $\\mu$ ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu$.)\n",
    "2. Subtracting the per pixel mean $\\mu_{ij}$  ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu_{ij}$.)\n",
    "3. Subtracting the mean $\\mu$ and dividing by the standard deviation $\\sigma$.\n",
    "4. Subtracting the pixel-wise mean $\\mu_{ij}$ and dividing by the pixel-wise standard deviation $\\sigma_{ij}$.\n",
    "5. Rotating the coordinate axes of the data.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "3, 4, 5\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print('One loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true,
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('No loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 22.044176 seconds\n",
      "One loop version took 38.168096 seconds\n",
      "No loop version took 0.219276 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# You should see significantly faster performance with the fully vectorized implementation!\n",
    "\n",
    "# NOTE: depending on what machine you're using, \n",
    "# you might not see a speedup when you go from two loops to one loop, \n",
    "# and might even see a slow-down."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "pass\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "pass\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-84e86719b989>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# plot the raw observations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mk_choices\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0maccuracies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mk_to_accuracies\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maccuracies\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracies\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 1"
     ]
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 1\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 3**\n",
    "\n",
    "Which of the following statements about $k$-Nearest Neighbor ($k$-NN) are true in a classification setting, and for all $k$? Select all that apply.\n",
    "1. The decision boundary of the k-NN classifier is linear.\n",
    "2. The training error of a 1-NN will always be lower than that of 5-NN.\n",
    "3. The test error of a 1-NN will always be lower than that of a 5-NN.\n",
    "4. The time needed to classify a test example with the k-NN classifier grows with the size of the training set.\n",
    "5. None of the above.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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